通过机器学习预测豆类中的宏观元素含量
1Department of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Rize, Turkey. muhammed.catal@erdogan.edu.tr.
Scientific reports
|October 6, 2025
概括
机器学习模型可以准确预测豆类的营养含量. 多变量自适应回归 (MARS) 模型在预测精准农业应用中的,,和水平方面表现出卓越的表现.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 营养素分析 营养素分析
背景情况:
- 豆类的营养质量对于动物料和土壤健康至关重要.
- 准确预测宏元素度 (P,K,Ca,Mg) 是优化农业实践的必要条件.
- 现有的营养分析方法可能耗时且资源密集.
研究的目的:
- 开发和比较机器学习模型,用于预测豆类物种中的宏元素度.
- 为此预测任务确定最有效的机器学习算法.
- 通过对豆类进行有效的营养评估,为精准农业做出贡献.
主要方法:
- 收集了10种豆类种类的料质量特征的综合数据集.
- 采用了四种机器学习算法:多变量自适应回归支柱 (MARS),K-最近邻居 (KNN),支持向量回归 (SVR) 和人工神经网络 (ANN).
- 使用RMSE,MAE和R2.2等统计指标评估模型性能.
主要成果:
- 在预测宏观元素度方面,MARS模型总体上表现优于KNN,SVR和ANN.
- 对于大多数元素,MARS实现了最低的RMSE和最高的R2值.
- KNN显示出合理的预测能力,而SVR和ANN的表现不那么有效,可能是由于数据集大小的限制.
结论:
- 马尔斯模型最适合预测研究的豆类物种中的宏观元素含量.
- 机器学习为评估豆类营养质量提供了强大而准确的方法.
- 这项研究通过提供有效的营养分析工具来推进精准农业.
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